Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Modelling global arbovirus disease ecology, transmission and importation risks
Thesis (PhD)--Stellenbosch University, 2025.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Published: |
Stellenbosch : Stellenbosch University
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613868758401024 |
|---|---|
| access_status_str | Open Access |
| author | Poongavanan, Jenicca |
| author2 | De Oliveira, Tulio |
| author_browse | De Oliveira, Tulio Poongavanan, Jenicca |
| author_facet | De Oliveira, Tulio Poongavanan, Jenicca |
| author_sort | Poongavanan, Jenicca |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/134780 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:42:59.065Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/134780 Modelling global arbovirus disease ecology, transmission and importation risks Poongavanan, Jenicca De Oliveira, Tulio Tegally, Houriiyah Dunaiski, Marcel Stellenbosch University. Faculty of Medicine and Health Sciences. Centre for Bioinformatics and Computational Biology. Arbovirus infections -- Transmission Dengue -- Transmission -- Mathematical models Phylogeography Communicable diseases -- Risk assessment Climatic changes -- Health aspects UCTD Thesis (PhD)--Stellenbosch University, 2025. Poongavanan, J. 2025. Modelling Global Arbovirus Disease Ecology, Transmission and Importation Risks. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/4c519006-d442-46dc-9b75-7ae0d8e87709 ENGLISH ABSTRACT: The global burden of arboviruses, including dengue virus (DENV) and Oropouche virus (OROV), which continue to re-emerge, could be exacerbated by climate change, urbanisation, and international mobility. However, surveillance limitations and spatial heterogeneity in data availability, particularly across Africa, have hindered accurate risk estimations and the development of effective control strategies. This thesis presents spatially explicit models that address key gaps in risk estimation across different stages of arbovirus emergence. Specifically, it investigates: (i) how to identify African regions most at risk of dengue introduction using international air travel data; (ii) how to estimate fine-scale dengue incidence in the absence of granular case data; (iii) how pseudo-absence sampling design influences the predictive performance of ecological niche models for emerging arboviruses; and (iv) what spatial and ecological factors shape the transmission risk and dispersal of OROV within South America and Brazil. In Chapter 2, we aim to identify African regions most at risk of dengue introduction using air travel data and local transmission suitability estimates. We develop a risk model integrating flight volumes with transmission potential and population density to identify areas where importation risk overlaps with conditions for local transmission, which can inform targeted surveillance. Chapter 3 addresses the challenge of generating fine-scale dengue incidence estimates in the absence of granular case data. We apply a disaggregation regression model to subnational dengue case data from Latin America and project it to Africa to produce high-resolution incidence estimates. We then compare these predictions with available national case reports and assess how they align with other existing dengue risk indicators, including environmental and vector suitability maps. In Chapter 4, we evaluate how five pseudo-absence sampling strategies influence the performance of ecological niche models for OROV. Building on this, we generate refined predictions of environmental suitability across the Americas, explore seasonal patterns of OROV transmission in Brazil, and assess areas of spatial overlap between the virus and its primary vector, Culicoides paraensis to identify zones of potential co-suitability and expansion risk. Finally, Chapter 5 explores how OROV has spread through Brazil by combining phylogeographic reconstruction with environmental and landscape modeling. We trace long-distance viral dispersal events and identify ecological features that shape OROV’s expansion. Together, these studies demonstrate the value of combining diverse and adaptable modeling approaches to improve arboviral risk prediction. By integrating ecological, epidemiological, and mobility data, the thesis produces high-resolution, spatially explicit outputs that offer more accurate and context-sensitive estimates across different stages of disease emergence. The work contributes novel methods and practical tools that enhance the precision, versatility, and public health relevance of arboviral risk mapping. AFRIKAANSE OPSOMMING: Die globale las van arbovirusse, insluitend denguevirus (DENV) en Oropouche-virus (OROV), wat gereeld epidemies veroorsaak, kan vererger word deur klimaatsverandering, verstedeliking en internasionale mobiliteit. Limitasies met betrekking tot databeskikbaarheid en ruimtelike ongelykhede in terme van patogeen naspooring en in, veral in Afrika, belemmer egter akkurate risikoskatting en die ontwikkeling van doeltreffende beheerstrategieë. Hierdie proefskrif bied aan ruimtelik-ekspressiewe modelle wat gapings in risikoskatting aanspreek oor verskillende fases van arbovirus-epidemies. Dit ondersoek spesifiek: (i) hoe om Afrikastreke te identifiseer wat die grootste risiko vir dengue-inportasies loop deur internasionale lugreissdata te gebruik; (ii) hoe om dengue-insidensie te skat in die afwesigheid van gedetailleerde gevaldata; (iii) hoe pseudo-afwesigheidsteekproefontwerp die voorspellingsprestasie van ekologiese nismodelle vir opkomende arbovirusse beïnvloed; en (iv) watter ruimtelike en ekologiese faktore die transmissierisiko en verspreiding van OROV binne Suid-Amerika en Brasilië beinvloed. In Hoofstuk 2 poog ons om Afrikastreke te identifiseer wat die grootste risiko loop vir die invoering van dengue, deur lugreissdata en plaaslike transmissiegeskiktheid in te sluit. Ons ontwikkel ’n risikomodel wat vlugvolume met transmissiepotensiaal en bevolkingsdigtheid integreer om gebiede te identifiseer waar invoerrisiko oorvleuel met gunstige toestande vir plaaslike transmissie, wat geteikende patogeen naspooring kan ondersteun. Hoofstuk 3 spreek die uitdaging aan om dengue-insidensie te skat op ‘n fyn skaal waar gedetailleerde gevaldata ontbreek. Ons pas ’n disaggregasie-regressiemodel toe op subnasionale dengue-gevaldata uit Latyns-Amerika en projekteer dit na Afrika om hoë-resolusie insidensieskattings te produseer. Hierdie voorspellinge word dan vergelyk met beskikbare nasionale gevalverslae en geëvalueer teenoor ander dengue-risiko-aanwysers, insluitend ekologiese en vektor-geskiktheidskaarte. In Hoofstuk 4 evalueer ons hoe vyf verskillende pseudo-afwesigheidsteekproefstrategieë die prestasie van ekologiese nismodelle vir OROV beïnvloed. Hierop bou ons voort deur verfynde geskiktheidsvoorspellinge oor die Amerikas te genereer, seisoenale transmissiepatrone van OROV in Brasilië te ondersoek, en gebiede van ruimtelike oorvleueling tussen die virus en sy primêre vektor, Culicoides paraensis, te identifiseer om sones van potensiële mede-geskiktheid en uitbreidingsrisiko te bepaal. Ten slotte ondersoek Hoofstuk 5 hoe OROV deur Brasilië versprei het deur filogeografiese rekonstruksie te kombineer met omgewings- en landskapmodellering. Ons spoor langafstand-virale verspreiding op en identifiseer ekologiese faktore wat OROV se uitbreidingspotensiaal vorm. Gesamentlik toon hierdie studies die waarde van die kombinasie van uiteenlopende en aanpasbare modelbenaderings om arbovirus-risiko beter te voorspel. Deur ekologiese, epidemiologiese en mobiliteitsdata te integreer, lewer die proefskrif hoë-resolusie, ruimtelik-ekspressiewe uitsette wat meer akkurate en konteks-sensitiewe skattings bied oor verskillende stadiums van patogeen verspreiding. Die werk dra by tot nuwe metodes en praktiese hulpmiddels wat die akkuraatheid, veelsydigheid en openbare-gesondheidsrelevansie van arbovirus-risikokartering verbeter. Doctoral 2026-01-07T12:42:01Z 2026-01-07T12:42:01Z 2025-12 Thesis https://scholar.sun.ac.za/handle/10019.1/134780 Stellenbosch University xx, 3 numbered, 196 pages : illustrations, maps application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Arbovirus infections -- Transmission Dengue -- Transmission -- Mathematical models Phylogeography Communicable diseases -- Risk assessment Climatic changes -- Health aspects UCTD Poongavanan, Jenicca Modelling global arbovirus disease ecology, transmission and importation risks |
| title | Modelling global arbovirus disease ecology, transmission and importation risks |
| title_full | Modelling global arbovirus disease ecology, transmission and importation risks |
| title_fullStr | Modelling global arbovirus disease ecology, transmission and importation risks |
| title_full_unstemmed | Modelling global arbovirus disease ecology, transmission and importation risks |
| title_short | Modelling global arbovirus disease ecology, transmission and importation risks |
| title_sort | modelling global arbovirus disease ecology transmission and importation risks |
| topic | Arbovirus infections -- Transmission Dengue -- Transmission -- Mathematical models Phylogeography Communicable diseases -- Risk assessment Climatic changes -- Health aspects UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/134780 |
| work_keys_str_mv | AT poongavananjenicca modellingglobalarbovirusdiseaseecologytransmissionandimportationrisks |